A lot of research projects use Python and different Python packages/modules to achieve results. This page describes how to easily setup a workable python environment with your own python packages inside.
For new projects, you should in particular consider whether you want to use the 2.x or 3.x variant of Python. The two versions are not compatible and in some cases, you may have to use an older 2.7.x versions of Python due to some of your packages not working with Python 3.x.
At Abacus we maintain two variants of Python and several versions of each:
- python/2.7.11 (default)
- python-intel/2.7.11 (default)
The vanilla Python versions (
python) includes Python and a few
extra packages including in particular
below). For further information, have a look at the official Python
The Intel optimised version of Python (
python-intel) has been
compiled by Intel and includes a lot of widely used python packages
virtualenv, etc. for more information look at the official Intel
Python home page:
To use a particular version of python simply use
testuser@fe1:~$ module add python-intel/126.96.36.199
Adding extra packages
In many cases you'll need extra python packages for your project. In the following we describe two ways to do this. You should consider both of them and use the one most suitable way for your project.
As noted above, also consider using one of the
variants as this already contains many packages, including in
particular maybe some of the packages you need.
Adding extra packages #1 - using
In the simple case, you only need one/a few packages, and only need
this for yourself. In this case, use
pip install --user to
install the module your own home directory as shown below, i.e., first
module add to select the right python version, next use
testuser@fe1:~$ module add python-intel/188.8.131.52 testuser@fe1:~$ pip install --user Pillow Collecting Pillow Downloading Pillow-4.1.0-cp35-cp35m-manylinux1_x86_64.whl (5.7MB) 100% |████████████████████████████████| 5.7MB 204kB/s Collecting olefile (from Pillow) Downloading olefile-0.44.zip (74kB) 100% |████████████████████████████████| 81kB 8.6MB/s Building wheels for collected packages: olefile Running setup.py bdist_wheel for olefile ... done Stored in directory: /home/testuser/.cache/pip/wheels/20/... Successfully built olefile Installing collected packages: olefile, Pillow Successfully installed Pillow-4.1.0 olefile-0.44
Files are installed in your home directory (in
Things to consider:
- The packages are only available to your own user, not to anybody else.
- If you change the Python version selected with
module add, the module may not work, and you may have to redo this.
Adding extra packages #2 - using
virtualenv is a tool that can be used to create isolated Python
environments. In each environment you select the Python version and
Python packages needed for you project. If you keep old virtualenv
environments, it is possible to later redo some of the job scripts in
the exact same Python environment as when you ran the script the first
Creating the environment
The Python files need to be placed in a directory. In the following
examples we use
/work/sdutest/tensor to install our own version
of Tensorflow. You should instead use a
directory within one of your own project directories.
testuser@fe1:~$ module purge testuser@fe1:~$ # tensorflow also requires the CUDA and cudnn modules testuser@fe1:~$ module add python/3.5.2 cuda/8.0.44 cudnn/5.1 testuser@fe1:~$ virtualenv /work/sdutest/tensor-1.2 PYTHONHOME is set. You *must* activate the virtualenv before using it Using base prefix '/opt/sys/apps/python/3.5.2' New python executable in /work/sdutest/tensor-1.2/bin/python3.5 Also creating executable in /work/sdutest/tensor-1.2/bin/python Installing setuptools, pip, wheel...done. testuser@fe1:~$ source /work/sdutest/tensor-1.2/bin/activate (tensor-1.2) testuser@fe1:~$ # you are now inside your own Python environment
Note the line with
/work/sdutest/tensor-1.2/bin/activate. You'll need to repeat this
step every time before you actually use your new Python environment.
We suggest to edit the
activate script to include the
module add lines from above to easily setup the
correct environment every time you use this. The two lines must be
added to the top of the file.
testuser@fe1:~$ nano /work/sdutest/tensor-1.2/bin/activate # add module purge and module add ... lines at the top
After the initial package setup, you can use
pip install as you
would if you had installed Python yourself, e.g.,
testuser@fe1:~$ source /work/sdutest/tensor-1.2/bin/activate (tensor-1.2) testuser@fe1:~$ which pip /work/sdutest/tensor-1.2/bin/pip (tensor-1.2) testuser@fe1:~$ pip3 install --upgrade tensorflow-gpu Collecting tensorflow-gpu Downloading tensorflow_gpu-1.1.0-cp35-cp35m-manylinux1_x86_64.whl (84.1MB) 100% |████████████████████████████████| 84.1MB 18kB/s Collecting protobuf>=3.2.0 (from tensorflow-gpu) ... Installing collected packages: protobuf, numpy, werkzeug, tensorflow-gpu Successfully installed numpy-1.12.1 protobuf-3.3.0 tensorflow-gpu-1.1.0 werkzeug-0.12.2 (tensor-1.2) testuser@fe1:~$
Using the environment
If you added the
module purge and
module add ... lines as
described in the first step, you simply need to
activate script everytime before starting to use the Python
testuser@fe1:~$ source /work/sdutest/tensor-1.2/bin/activate (tensor-1.2) testuser@fe1:~$ # you are now inside your own Python environment
Similarly, in your Slurm job scripts you should add the
line as shown below:
#! /bin/bash #SBATCH --account sdutest_gpu # account #SBATCH --time 2:00:00 # max time (HH:MM:SS) echo Running on "$(hostname)" echo Available nodes: "$SLURM_NODELIST" echo Slurm_submit_dir: "$SLURM_SUBMIT_DIR" echo Start time: "$(date)" # Load the Python environment source /work/sdutest/tensor-1.2/bin/activate # Start your python application python ... echo Done.